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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 571–580 of 796 papers

TitleStatusHype
Prognostics of Surgical Site Infections using Dynamic Health Data—0
Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation—0
Spectral Inference Methods on Sparse Graphs: Theory and Applications—0
Unorganized Malicious Attacks Detection—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
Max-Norm Optimization for Robust Matrix Recovery—0
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model—0
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